Safety and Bias Mitigation in Fine-Tuned Models Training Course
Safety and bias mitigation in fine-tuned models is an increasing priority as AI integration deepens across various industries and regulatory standards continue to develop.
This instructor-led live training, available online or onsite, targets intermediate-level machine learning engineers and AI compliance professionals seeking to identify, assess, and mitigate safety risks and biases within fine-tuned language models.
Upon completion of this training, participants will be capable of:
- Grasping the ethical and regulatory landscape governing safe AI systems.
- Recognizing and assessing prevalent forms of bias in fine-tuned models.
- Implementing bias mitigation strategies during and after the training phase.
- Designing and auditing models to ensure safety, transparency, and fairness.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live-lab environment.
Customization Options
- For tailored training requests, please contact us to arrange your session.
Course Outline
Foundations of Safe and Fair AI
- Core concepts: safety, bias, fairness, and transparency
- Categories of bias: dataset, representation, and algorithmic
- Overview of regulatory frameworks (e.g., EU AI Act, GDPR)
Bias in Fine-Tuned Models
- Understanding how fine-tuning can introduce or amplify bias
- Case studies and real-world failures
- Identifying bias in datasets and model predictions
Techniques for Bias Mitigation
- Data-level strategies (rebalancing, augmentation)
- In-training strategies (regularization, adversarial debiasing)
- Post-processing strategies (output filtering, calibration)
Model Safety and Robustness
- Detecting unsafe or harmful outputs
- Handling adversarial inputs
- Red teaming and stress testing fine-tuned models
Auditing and Monitoring AI Systems
- Bias and fairness evaluation metrics (e.g., demographic parity)
- Explainability tools and transparency frameworks
- Ongoing monitoring and governance practices
Toolkits and Hands-On Practice
- Utilizing open-source libraries (e.g., Fairlearn, Transformers, CheckList)
- Practical session: Detecting and mitigating bias in a fine-tuned model
- Generating safe outputs through prompt design and constraints
Enterprise Use Cases and Compliance Readiness
- Best practices for integrating safety into LLM workflows
- Documentation and model cards for compliance
- Preparing for audits and external reviews
Summary and Next Steps
Requirements
- A solid understanding of machine learning models and training methodologies
- Practical experience with fine-tuning and Large Language Models (LLMs)
- Familiarity with Python and Natural Language Processing (NLP) concepts
Target Audience
- AI compliance teams
- Machine Learning engineers
Open Training Courses require 5+ participants.
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